MOEA/D-S3: MOEA/D using SVM-based Surrogates adjusted to Subproblems for Many objective optimization

This paper proposes a surrogate-assisted MOEA/D using SVM-based surrogates adjusted to subproblems (MOEA/D-S-3), which intends to achieve the following technical advantages. Firstly, in order to construct a proper surrogate while reducing learning cost to construct surrogates, a surrogate is an SVM-classifier that identifies a specific region of good solutions and thus its learning cost should be lower than a popular alternative approach, i.e., fitness approximation. Secondly, relying on the first advantage, multiple surrogates are constructed and each surrogate, like an expert, is adjusted to each subproblem defined in the MOEA/D framework in order to improve diversity and convergence of the Pareto set. Experimental results show that MOEA/D-S-3 outperforms MOEA/D on a number of many objective benchmark problems.